98 lines
3.2 KiB
Plaintext
98 lines
3.2 KiB
Plaintext
#include "gqa_prefill_attn.cuh"
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#include <torch/extension.h>
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#ifndef ASTRAI_NO_MMA
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#include "gqa_prefill_attn_mma.cuh"
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#endif
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template <int HEAD_DIM>
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static void dispatch_prefill(GQAParams& p) {
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#ifndef ASTRAI_NO_MMA
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constexpr int WARPS = 4, BC = 32, BR = 16, LD = HEAD_DIM;
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dim3 grid((p.q_len + BR * WARPS - 1) / (BR * WARPS), p.q_head, p.batch);
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dim3 block(WARPS * 32, 1, 1);
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// shared sQ: single staging area (BR*LD), not per-warp
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int smem = (2 * BC * LD + BR * LD) * (int)sizeof(bf16);
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cudaFuncSetAttribute(gqa_prefill_attn_mma_kernel<HEAD_DIM, WARPS, BC>,
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cudaFuncAttributeMaxDynamicSharedMemorySize, smem);
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gqa_prefill_attn_mma_kernel<HEAD_DIM, WARPS, BC><<<grid, block, smem>>>(p);
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#else
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constexpr int G = 8, ROWS = 32, P_BC = 32;
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dim3 grid((p.q_len + ROWS - 1) / ROWS, p.q_head, p.batch);
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dim3 block(G, ROWS, 1);
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size_t smem = 2 * P_BC * HEAD_DIM * sizeof(bf16);
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gqa_prefill_attn_kernel_t<HEAD_DIM, G, ROWS, P_BC><<<grid, block, smem>>>(p);
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#endif
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}
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torch::Tensor gqa_prefill_attn(
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torch::Tensor q,
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torch::Tensor k,
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torch::Tensor v,
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c10::optional<torch::Tensor> mask,
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bool is_causal = false,
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int64_t causal_offset = 0,
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c10::optional<double> scale = c10::nullopt
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) {
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TORCH_CHECK(q.is_cuda() && k.is_cuda() && v.is_cuda());
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TORCH_CHECK(q.dtype() == torch::kBFloat16);
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TORCH_CHECK(k.dtype() == torch::kBFloat16);
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TORCH_CHECK(v.dtype() == torch::kBFloat16);
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GQAParams p;
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p.batch = q.size(0);
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p.q_head = q.size(1);
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p.kv_head = k.size(1);
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p.q_len = q.size(2);
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p.kv_len = k.size(2);
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p.head_dim = q.size(3);
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TORCH_CHECK(p.head_dim % 16 == 0, "head_dim must be multiple of 16");
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p.use_mask = mask.has_value();
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p.is_causal = (int)is_causal;
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p.causal_offset = (int)causal_offset;
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p.scale = scale.has_value() ? (float)scale.value() : 1.0f / sqrtf((float)p.head_dim);
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p.q = (const bf16*)q.data_ptr();
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p.k = (const bf16*)k.data_ptr();
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p.v = (const bf16*)v.data_ptr();
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if (p.use_mask) {
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TORCH_CHECK(mask.value().dtype() == torch::kBool);
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TORCH_CHECK(mask.value().dim() == 2);
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TORCH_CHECK(mask.value().size(0) == p.batch);
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TORCH_CHECK(mask.value().size(1) == p.kv_len);
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p.mask = mask.value().data_ptr<bool>();
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} else {
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p.mask = nullptr;
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}
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auto O = torch::empty_like(q);
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p.o = (bf16*)O.data_ptr();
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switch (p.head_dim) {
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case 64:
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dispatch_prefill<64>(p);
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break;
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case 128:
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dispatch_prefill<128>(p);
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break;
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case 256:
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dispatch_prefill<256>(p);
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break;
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default:
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TORCH_CHECK(false, "prefill: unsupported head_dim ", p.head_dim,
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" (supported: 64,128,256)");
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}
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return O;
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}
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PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
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m.def("gqa_prefill_attn", &gqa_prefill_attn,
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py::arg("q"),
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py::arg("k"),
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py::arg("v"),
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py::arg("mask") = py::none(),
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py::arg("is_causal") = false,
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py::arg("causal_offset") = 0,
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py::arg("scale") = py::none(),
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"GQA prefill (tensor-core mma on sm_80+, scalar fallback)");
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}
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